Many modern consumer devices require comfortable user data entry and proximity sensing interfaces which became very popular in devices like smart phones and portable media players. A variety of two-dimensional capacitive proximity position sensors are known. A common two-dimensional sensor implementation comprises a matrix organization of capacitive sensors that are driven by row- and column drive signals and the resulting coupled charge on a sensing node is measured to yield a measure for the capacitive coupling. A position is obtained by calculating the position's x- and y-component separately by applying a center-of-gravity formula that assigns weights to the sensor signals related to the position of the sensor itself. The sum of the weighted sensor activity levels is divided by the sum of all un-weighted activity levels, yielding the averaged x- and y-position components. A drawback of this approach is the need for a plurality of sensors that increase the complexity of interfacing with a controller circuit. The matrix approach results in a long conversion time as every sensor capacitors activity level has to be evaluated for the detection of a touch event.
The measurement principles applied in position sensing are many-fold. The following measurement principles are commonly applied: Capacitive Proximity Position Sensors, Resistive Position Sensors, Optical Position Sensors and Acoustical Position Sensors.
The first two principles are the most popular ones and cover in total more than 90% of all position sensing applications. The measurement of touch dependent resistances and proximity dependent capacitances is utilized to obtain the position information by numerical post-processing. An integrator circuit is used to transform a resistance or a capacitance into timing information that can be captured by a microcontroller unit (MCU). The integrator is stimulated by an input signal and the resulting response is sampled and hold and evaluated by an MCU. Another common approach is to use a constant current to charge a capacitor under test and measure the time required to charge the capacitor to a predefined voltage. After the measurement, the capacitor is reset by a reset signal and a new charging cycle can be started. Another common method for capacitance measurement is to use a capacitor under test as timing element in a relaxation oscillator, resulting in a capacitance to frequency conversion. The resulting frequency is measured by a frequency measurement routine executed on a MCU.
The before mentioned measurement principles are not very flexible in terms of software-based configuration, especially a two-dimensional position calculation is not provided. Besides that, special analog circuitry is required to implement these measurement principles. Especially the integration of analog circuitry adds complexity to an existing digital design and moreover in many cases additional process options are required that add unwanted cost. Monolithic integrations of proximity sensing devices are available, but these devices contribute to the device count (BOM) of a target system and furthermore contribute to the power consumption of the target system. Two-dimensional proximity position sensor device (i.e. for touch screens) are generally high-pin-count devices.
A common capacitance measurement method is to use the capacitor under test as a frequency dependent resistor charging an integration capacitor in a switched capacitor integrator configuration. The basic principle is well known and documented, e.g., in the publications: Switched-Capacitor Circuit Design, R. Gregorian, et al, Proceedings of the IEEE, Vol 71, No. 8, August 1983 and Switched-Capacitor Circuit with Large Time Constant, Krishnaswamy Nagaraj, U.S. Pat. No. 4,894,620, Jan. 16, 1990.
Common implementations of switched capacitor based position sensing devices share the same approach wherein one sensing capacitor is evaluated at a time. In order to remove noise, an n-key-rollover scheme is applied that evaluates a sensor capacitance multiple times and applies a filter function on the sample series in order to remove high frequent noise components. This approach results in long processing time which is hardly acceptable, for example for online-handwriting recognition systems.